光学连贯度的血管密度特征 断层扫描 血管学 用机器学习对光学神经病变进行分类
Jalil Jalili1, Mohadeseh Nadimi, Behzad Jafari
1Biomedical Engineering Unit (JJ, MN), Cardiovascular Disease Research Center, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran; Farabi Eye Hospital (BJ, AE, MAF), Tehran University of Medical Sciences, Tehran, Iran; School of Medicine (MM), Guilan University of Medical Sciences, Rasht, Iran; and Department of Ophthalmology (PSS), University of Colorado, School of Medicine, Aurora, Colorado.
概括
机器学习使用光学连贯断层扫描 (OCT-A) 血管密度特征准确分类视觉神经炎 (ON) 和非动脉炎前侧缺血性视觉神经病变 (NAION). 这种方法对区分这些视神经疾病有希望.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 在适当的患者管理中,区分健康的眼睛,非动脉炎前部缺血性视觉神经病变 (NAION) 和视觉神经炎 (ON) 是至关重要的.
- 光学连贯断层扫描血管造影 (OCT-A) 提供了视神经头部血管系统的详细成像.
研究的目的:
- 评估机器学习 (ML) 模型的分类性能,使用来自OCT-A.的周周血管密度特征.
- 使用这些ML模型来区分健康的眼睛,NAION和ON.
主要方法:
- 分析了来自45名NAION患者,32名ON患者和76名健康个体的OCT-A图像.
- 通过基于值的细分,提取了四个周周血管密度特征.
- 使用支持向量机 (SVM),随机森林 (RF) 和高斯天真贝叶斯 (GNB) 分类器,并使用AUC和精度进行评估和评估.
主要成果:
- 对于ON与NAION分类,SVM,RF和GNB实现了AUC为1和精度为1以50%的门.
- 对于ON与正常分类,SVM和RF实现了AUC为1和精度为1的100%值.
- 对于NAION与正常分类,SVM和RF实现了1的AUC和1的精度,50%的值.
结论:
- 机器学习模型利用结合的周状血管密度特征,在区分NAION和ON方面表现出色.
- 该研究强调了OCT-A衍生特征和ML的潜力,以准确诊断视神经病变.
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